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Repository Link:
STOC'D GitHub RepositoryI've been working on a project called STOC'D (Stochastic Trade Optimization for Credit Derivatives) for some time, and it's grown into a pretty extensive codebase. To be honest, I've hit a point where I’m feeling a bit burnt out working on it alone, and I’m making it public to hopefully get some fresh eyes on it and maybe some help from this awesome community!
What STOC'D Is:
STOC'D is a tool designed for options traders looking to optimize credit spread strategies. It employs a range of stochastic models and volatility estimation techniques to analyze and identify optimal trade setups for bull put and bear call spreads. If you're into options trading, especially credit spreads, this tool might have some features that you'll find helpful.How It Works:
- Options Data Analysis: Fetches historical price data and options chains via the Tradier API.
- Volatility Estimation: Utilizes models like Yang-Zhang, Rogers-Satchell, and Heston for more accurate volatility calculations.
- Stochastic Models: Includes models like Black-Scholes-Merton, Heston, Kou, and CGMY to simulate option pricing and predict spread profitability.
- Risk Assessment: Calculates VaR (Value at Risk), Expected Shortfall (ES), and potential profit/loss based on Monte Carlo simulations.
- Spread Identification: Identifies optimal spreads based on customizable criteria (Days to Expiration, Return on Risk, etc.) and ranks them using a composite scoring system.
Why I'm Sharing:
STOC'D is now public because it's at a stage where I could really use feedback and contributions to improve its functionality. The tool has grown into something larger than I initially anticipated, and I believe it has a lot of potential, but it’s tough keeping the momentum going solo. If you're into trading, stochastic modeling, or Go programming, I'd love to have you check it out and maybe help me out!How You Can Help:
- Review the code: Whether you’re a quant or a Go developer, feedback on the code and models would be super helpful.
- Contribute: I'm looking for contributors who can help implement new models, optimize existing code, or work on expanding features like multi-asset simulations, improved spread identification, etc.
- Discuss: I’m open to any suggestions for improvement, whether it’s new features, better performance, or additional models to include.
